PENGGUNAAN ARTIFICIAL INTELLIGENCE (AI) SEBAGAI STRATEGI KOMPETITIF DI MEDIA TELEVISI TVONE

Safira, Seera (2024) PENGGUNAAN ARTIFICIAL INTELLIGENCE (AI) SEBAGAI STRATEGI KOMPETITIF DI MEDIA TELEVISI TVONE. Tesis (S2) - thesis, Universitas Bakrie.

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Abstract

Innovation and advanced technology, such as artificial intelligence (AI), provide a significant competitive edge in television by attracting viewers. This study explores tvOne's AI management and how it creates a sustainable competitive advantage in the news sector compared to its rivals while addressing existing challenges. A qualitative research method, using competitive advantage theory and the "pipelines" strategy, was applied. This strategy emphasizes controlling and optimizing distribution channels and monetizing content effectively. Successful companies in this area generally have strong infrastructure, engaging content, and the ability to adapt to media industry changes. tvOne applies this strategy by integrating AI as a new business area. The study finds that tvOne’s use of AI presenters positively impacts branding and introduces innovative, cutting-edge technology. tvOne’s AI development focuses on acceleration and efficiency with extensive knowledge data. The AI learning model includes trAIning with collected data and using advanced natural language processing algorithms. The mAIn challenge is synchronizing and trAIning the news production team on AI. Keywords: Artificial Intelligence (AI), television innovation, AI management, AI presenter, tvOne.

Item Type:	Thesis (Tesis (S2) - )
Uncontrolled Keywords:	Artificial Intelligence (AI), television innovation, AI management, AI presenter, tvOne.
Subjects:	Management > Corporate Sustainability Strategy Computer Science > Information Systems Communication Science > Mass Media Communication Communication Science > News Broadcast Communication Science > Social Media Thesis > Thesis (S2)
Divisions:	Fakultas Ekonomi dan Ilmu Sosial > Program Studi Manajemen
Depositing User:	Seera Safira
Date Deposited:	21 Aug 2024 02:14
Last Modified:	21 Aug 2024 02:14
URI:	https://repository.bakrie.ac.id/id/eprint/9848

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